Denoising Diffusion-Based Image Generation Model Using Principal Component Analysis
In recent years, advancements in GPU technology and increased data collection have significantly enhanced the performance of artificial intelligence and image generation models. However, in specific areas such as medical imaging or facial images, constraints in data collection and class imbalance is...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10755095/ |
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| author | Myung Keun Song Asim Niaz Muhammad Umraiz Ehtesham Iqbal Shafiullah Soomro Kwang Nam Choi |
| author_facet | Myung Keun Song Asim Niaz Muhammad Umraiz Ehtesham Iqbal Shafiullah Soomro Kwang Nam Choi |
| author_sort | Myung Keun Song |
| collection | DOAJ |
| description | In recent years, advancements in GPU technology and increased data collection have significantly enhanced the performance of artificial intelligence and image generation models. However, in specific areas such as medical imaging or facial images, constraints in data collection and class imbalance issues have posed challenges to improving image quality. This study proposes the integration of Principal Component Analysis (PCA) into image generation models to address these challenges. Specifically, to overcome the limitations of conventional image generation models like GANs and VAEs, we utilize the Denoise Diffusion Probabilistic Model (DDPM) as the backbone, integrating it with PCA techniques. Using the CIFAR10 and FFHQ datasets, we evaluated the image quality of the proposed PCA-DDPM, the traditional DDPM, and DCGAN. As a result, the PCA-DDPM demonstrated superior image quality and efficiency. Notably, it maintained high performance even when trained with a limited amount of data. The findings of this research contribute significantly to the advancement of image generation technology and are expected to be applied in various domains. |
| format | Article |
| id | doaj-art-54c6705fc36c4bf9a7ee09c605fcc0b6 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-54c6705fc36c4bf9a7ee09c605fcc0b62025-08-20T02:01:54ZengIEEEIEEE Access2169-35362024-01-011217048717049810.1109/ACCESS.2024.350021210755095Denoising Diffusion-Based Image Generation Model Using Principal Component AnalysisMyung Keun Song0https://orcid.org/0009-0003-8235-2228Asim Niaz1https://orcid.org/0000-0003-3905-9774Muhammad Umraiz2Ehtesham Iqbal3Shafiullah Soomro4https://orcid.org/0000-0002-4318-5055Kwang Nam Choi5https://orcid.org/0000-0002-7420-9216Department of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of KoreaDepartment of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of KoreaDepartment of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of KoreaAdvanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesDepartment of Computer Science and Media Technology, Linnaeus University, Växjö, SwedenDepartment of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of KoreaIn recent years, advancements in GPU technology and increased data collection have significantly enhanced the performance of artificial intelligence and image generation models. However, in specific areas such as medical imaging or facial images, constraints in data collection and class imbalance issues have posed challenges to improving image quality. This study proposes the integration of Principal Component Analysis (PCA) into image generation models to address these challenges. Specifically, to overcome the limitations of conventional image generation models like GANs and VAEs, we utilize the Denoise Diffusion Probabilistic Model (DDPM) as the backbone, integrating it with PCA techniques. Using the CIFAR10 and FFHQ datasets, we evaluated the image quality of the proposed PCA-DDPM, the traditional DDPM, and DCGAN. As a result, the PCA-DDPM demonstrated superior image quality and efficiency. Notably, it maintained high performance even when trained with a limited amount of data. The findings of this research contribute significantly to the advancement of image generation technology and are expected to be applied in various domains.https://ieeexplore.ieee.org/document/10755095/Artificial intelligencedeep learningdenoising diffusionimage generationprincipal component analysis |
| spellingShingle | Myung Keun Song Asim Niaz Muhammad Umraiz Ehtesham Iqbal Shafiullah Soomro Kwang Nam Choi Denoising Diffusion-Based Image Generation Model Using Principal Component Analysis IEEE Access Artificial intelligence deep learning denoising diffusion image generation principal component analysis |
| title | Denoising Diffusion-Based Image Generation Model Using Principal Component Analysis |
| title_full | Denoising Diffusion-Based Image Generation Model Using Principal Component Analysis |
| title_fullStr | Denoising Diffusion-Based Image Generation Model Using Principal Component Analysis |
| title_full_unstemmed | Denoising Diffusion-Based Image Generation Model Using Principal Component Analysis |
| title_short | Denoising Diffusion-Based Image Generation Model Using Principal Component Analysis |
| title_sort | denoising diffusion based image generation model using principal component analysis |
| topic | Artificial intelligence deep learning denoising diffusion image generation principal component analysis |
| url | https://ieeexplore.ieee.org/document/10755095/ |
| work_keys_str_mv | AT myungkeunsong denoisingdiffusionbasedimagegenerationmodelusingprincipalcomponentanalysis AT asimniaz denoisingdiffusionbasedimagegenerationmodelusingprincipalcomponentanalysis AT muhammadumraiz denoisingdiffusionbasedimagegenerationmodelusingprincipalcomponentanalysis AT ehteshamiqbal denoisingdiffusionbasedimagegenerationmodelusingprincipalcomponentanalysis AT shafiullahsoomro denoisingdiffusionbasedimagegenerationmodelusingprincipalcomponentanalysis AT kwangnamchoi denoisingdiffusionbasedimagegenerationmodelusingprincipalcomponentanalysis |